




"""simple docstring"""






from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
				import timm


if is_torch_available():
				from torch import Tensor




class 	a__      (							_A       ,						_A	):
						snake_case__							   =			'''pixel_values'''
						snake_case__							   =			False
						snake_case__							   =			TimmBackboneConfig





						def __init__(				self   :						str  ,a__   :						Any  ,**a__   :						Optional[Any])    ->							Any:




											"""simple docstring"""







											requires_backends(self  ,'''timm''')
											super().__init__(a_)
											_lowerCAmelCase:List[str]						=	config

											if config.backbone is None:
																raise ValueError('''backbone is not set in the config. Please set it to a timm model name.''')

											if config.backbone not in timm.list_models():
																raise ValueError(F'backbone {config.backbone} is not supported by timm.')

											if hasattr(a_  ,'''out_features''') and config.out_features is not None:
																raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.''')

											_lowerCAmelCase:int						=	getattr(a_  ,'''use_pretrained_backbone'''  ,a_)
											if pretrained is None:
																raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.''')

											# We just take the final layer by default. This matches the default for the transformers models.
											_lowerCAmelCase:Tuple						=	config.out_indices if getattr(a_  ,'''out_indices'''  ,a_) is not None else (-1,)

											_lowerCAmelCase:Union[str, Any]						=	timm.create_model(
											    config.backbone  ,pretrained=a_  ,features_only=config.features_only  ,in_chans=config.num_channels  ,out_indices=a_  ,**a_  ,)
											# These are used to control the output of the model when called. If output_hidden_states is True, then
											# return_layers is modified to include all layers.
											_lowerCAmelCase:Dict						=	self._backbone.return_layers
											_lowerCAmelCase:Optional[Any]						=	{layer["module"]: str(a_) for i, layer in enumerate(self._backbone.feature_info.info)}
											super()._init_backbone(a_)





						@classmethod
						def    __UpperCamelCase  (				cls   :						Optional[Any]  ,a__   :						Dict  ,*a__   :						Union[str, Any]  ,**a__   :						int)    ->							List[Any]:




											"""simple docstring"""







											requires_backends(cls  ,['''vision''', '''timm'''])
											from ...models.timm_backbone import TimmBackboneConfig

											_lowerCAmelCase:Union[str, Any]						=	kwargs.pop('''config'''  ,TimmBackboneConfig())

											_lowerCAmelCase:str						=	kwargs.pop('''use_timm_backbone'''  ,a_)
											if not use_timm:
																raise ValueError('''use_timm_backbone must be True for timm backbones''')

											_lowerCAmelCase:str						=	kwargs.pop('''num_channels'''  ,config.num_channels)
											_lowerCAmelCase:Optional[Any]						=	kwargs.pop('''features_only'''  ,config.features_only)
											_lowerCAmelCase:Union[str, Any]						=	kwargs.pop('''use_pretrained_backbone'''  ,config.use_pretrained_backbone)
											_lowerCAmelCase:Dict						=	kwargs.pop('''out_indices'''  ,config.out_indices)
											_lowerCAmelCase:str						=	TimmBackboneConfig(
											    backbone=a_  ,num_channels=a_  ,features_only=a_  ,use_pretrained_backbone=a_  ,out_indices=a_  ,)
											return super()._from_config(a_  ,**a_)





						def    __UpperCamelCase  (				self   :						Optional[int]  ,a__   :						List[str])    ->							Dict:




											"""simple docstring"""







											pass







						def    __UpperCamelCase  (				self   :						List[str]  ,a__   :						List[Any]  ,a__   :						Optional[int]=None  ,a__   :						str=None  ,a__   :						Optional[Any]=None  ,**a__   :						str)    ->							int:




											"""simple docstring"""







											_lowerCAmelCase:Tuple						=	return_dict if return_dict is not None else self.config.use_return_dict
											_lowerCAmelCase:Union[str, Any]						=	(
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											_lowerCAmelCase:str						=	output_attentions if output_attentions is not None else self.config.output_attentions

											if output_attentions:
																raise ValueError('''Cannot output attentions for timm backbones at the moment''')

											if output_hidden_states:
																# We modify the return layers to include all the stages of the backbone
																_lowerCAmelCase:int						=	self._all_layers
																_lowerCAmelCase:Optional[Any]						=	self._backbone(a_  ,**a_)
																_lowerCAmelCase:List[Any]						=	self._return_layers
																_lowerCAmelCase:List[Any]						=	tuple(hidden_states[i] for i in self.out_indices)
											else:
																_lowerCAmelCase:Tuple						=	self._backbone(a_  ,**a_)
																_lowerCAmelCase:Any						=	None

											_lowerCAmelCase:Optional[Any]						=	tuple(a_)
											_lowerCAmelCase:Tuple						=	tuple(a_) if hidden_states is not None else None

											if not return_dict:
																_lowerCAmelCase:List[str]						=	(feature_maps,)
																if output_hidden_states:
																					_lowerCAmelCase:Dict						=	output + (hidden_states,)
																return output

											return BackboneOutput(feature_maps=a_  ,hidden_states=a_  ,attentions=a_)













'''simple docstring'''






import random
from typing import Any






def   __A    (				a_    :	list						):
		for _ in range(len(a_						)						):
				lowerCAmelCase  :     List[Any]       						= random.randint(0   ,len(a_						) - 1						)
				lowerCAmelCase  :     Tuple       						= random.randint(0   ,len(a_						) - 1						)
				lowerCAmelCase						,		lowerCAmelCase  :     Optional[Any]       						= data[b], data[a]
		return data


if __name__ == "__main__":
					lowerCAmelCase									=   [0, 1, 2, 3, 4, 5, 6, 7]
					lowerCAmelCase									=   ["""python""", """says""", """hello""", """!"""]
					print("""Fisher-Yates Shuffle:""")
					print("""List""", integers, strings)
					print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))



